# Neural Random Forests

@article{Biau2016NeuralRF, title={Neural Random Forests}, author={G{\'e}rard Biau and Erwan Scornet and Johannes Welbl}, journal={Sankhya A}, year={2016}, volume={81}, pages={347 - 386} }

Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural random forests. Both predictors exploit prior knowledge of regression trees for their architecture, have less parameters to tune than standard…

## 88 Citations

### Adaptive Bayesian Reticulum

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A probabilistic construct is proposed that exploits the idea of a node's unexplained potential in order to decide where to expand further, mimicking the standard tree construction in a Neural Network setting, alongside a modified gradient ascent that first locally optimizes an expanded node before a global optimization.

### Sparse Projection Oblique Randomer Forests

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### Neural Random Forest Imitation

- Computer ScienceArXiv
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This paper introduces a mechanism for designing the architecture of a Sparse Multi-Layer Perceptron network, for classification, called ForestNet, and exhibits very promising results, as the sparse networks performed similarly to their fully connected counterparts with a reduction of more than 98% of connections in the visual tasks.

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- Computer ScienceSDM
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